Instructions to use hf-tiny-model-private/tiny-random-GPTNeoModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-GPTNeoModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-GPTNeoModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 747 Bytes
6c8b246 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"activation_function": "gelu_new",
"architectures": [
"GPTNeoModel"
],
"attention_dropout": 0.0,
"attention_layers": [
"global",
"local",
"global",
"local"
],
"attention_types": [
[
[
"global",
"local"
],
2
]
],
"bos_token_id": 0,
"embed_dropout": 0.0,
"eos_token_id": 0,
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": null,
"layer_norm_epsilon": 1e-05,
"max_position_embeddings": 512,
"model_type": "gpt_neo",
"num_heads": 4,
"num_layers": 4,
"pad_token_id": 1023,
"resid_dropout": 0.0,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"use_cache": true,
"vocab_size": 1024,
"window_size": 7
}
|